Robust real-time EEG suppression detection device and method

ABSTRACT

The present invention relates to a physiological monitor and system, more particularly to an electroencephalogram (EEG) monitor and system, and a method of detecting the presence or occurrence of suppression in the EEG signal. Accurately detecting signal suppression in real-time provides the clinician with the ability to prevent possibly severe, long-term damage to patients as a result of excessive anesthetic or sedative. The present invention provides such a system and method for accurately and automatically detecting suppression in physiological, particularly EEG, signals in real-time and allowing for the administration of treatment or medication to reverse the effects of such situations, or minimize the harm caused. The present invention also allows for the use of closed-loop treatment or drug delivery systems to further automate the process and provide rapid treatment to a patient to reverse or minimize potential harm.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/216,755 which was filed on Aug. 24, 2011.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to the processing of signals, andparticularly to the processing of electrophysiological signals. Moreparticularly, the present invention relates to the processing ofelectroencephalographic signals. More particularly still, the presentinvention relates to the detection and identification of suppressionperiods in electroencephalographic signals. Further, the presentinvention relates to an automated method for identification anddetection of suppression periods in electroencephalographic signals.

2. Technology Review

Electroencephalography (EEG) is the recording of electrical activityfrom the scalp surface, which is produced by the firing of neurons inthe brain. In particular, the EEG is generated by cortical nerve cellinhibitory and excitatory postsynaptic potentials. These potentialssummate in the underlying cortex and extend to the scalp surface, wherethey are recorded as the EEG signals.

During deep anesthesia, the EEG may develop a peculiar pattern ofactivity known as suppression-burst pattern. This is a particular typeof pseudo-periodic pattern, consisting of bursts of high-voltageactivity (mixture of sharp and slow waves) periodically interrupted byepisodes of suppression (low-voltage activity <10 μV). The underlyingphysiological changes that accompany the suppression-burst patternduring general anesthesia consist of cortical neuronal inactivity duringsuppression, cortical depolarization during burst, and corticalhyperpolarization just prior to the onset of suppression.

Suppression-burst pattern may be induced intentionally to reducecerebral metabolic demand and provide possible brain protection, byadministering large doses of general anesthetics. Titration to aspecific degree of suppression-burst has been recommended as a surrogateend-point for barbiturate coma therapy. In addition, hypothermia hasbeen observed to emphasize the suppression effect of general anesthesia.When not associated with a high dose of anesthetic drugs or CNSdepressant drugs or hypothermia, the presence of suppression-burstpattern in EEG carries a grave prognosis due to its relation to severeencephalopathy. Some of the underlying neuropathological conditionswhich produce a suppression-burst pattern in EEG are head trauma,stroke, coma or anoxia.

Typically, the episodes of suppression are longer (typically 5-10seconds) than the bursts of activity (typically 1-3 seconds). As theanesthetic depth increases and/or the patient's condition worsens,bursts become shorter, simpler and of lower amplitude; and periods ofsuppression become longer until complete EEG suppression (activity <10μV) or electrocerebral silence (activity <2 μV) supervenes.

Due to the high clinical importance of the presence of suppression inEEG signals, its timely and robust detection is very important. Inparticular, it is extremely important to be able to accurately detectthe periods of EEG suppression in real-time during general anesthesia toavoid very deep anesthetic levels. The suppression periods in EEGwaveform can be manually detected by human EEG experts based on visualobservation. A trained individual would either view a printout of theEEG signal or view the EEG signal on a monitor in real time. The signalwould then be visually inspected for periods of time, typically longerthan 0.5 s, where the peak-to-peak amplitude of the EEG signal is muchless (typically 5 to 20 μV). However, this method is not very robust dueto the subjectivity in the visual analysis. In addition, it is verytime-consuming, tedious and expensive (due to the expertise required).Hence it is very important to develop an accurate and automated methodfor real-time EEG suppression detection. Therefore, the objective of thepresent invention is to provide an automated method for robust detectionof suppression periods in EEG signals in real time.

Current automated methodologies for the detection of suppression in EEGwaveform are typically carried out based on the peak-to-peak methodinherited from visual observation, as described above. However, thismethod is particularly sensitive to noise and can fail to detectsuppression in certain conditions due to the susceptibility of the EEGsignal, and thus the measured peak-to-peak amplitude of the said signal,to various kinds of artifacts. EEG signals can be corrupted by variousphysiological artifacts such as ocular artifacts (eye blinks, rapid eyemovements, etc.), muscle artifacts (head movement, biting, swallowing,facial movements, etc.) or ECG artifacts, as well as non-physiologicalartifacts such as electrode/lead movement, percussion from anintravenous drip, etc. These artifacts can falsely increase the measuredamplitude of the EEG signal and hence can affect the ability ofautomated peak-to-peak measurement methods to accurately detectsuppression in EEG signals.

It is therefore an objective of the present invention to provide adevice, system, and method that addresses all of these needs and otherswhere such a device, system, and method would be applicable. It isanother object of the present invention that this device and methoddetect EEG suppression in real-time.

SUMMARY OF THE INVENTION

The present invention relates to the processing of signals, andparticularly to the processing of electrophysiological signals. Moreparticularly, the present invention relates to the processing ofelectroencephalographic signals. More particularly still, the presentinvention relates to the detection and identification of suppressionperiods in electroencephalographic signals. Further, the presentinvention relates to an automated method for identification anddetection of suppression periods in electroencephalographic signals.

The accurate and real-time detection of the presence of suppression inan EEG signal allows for increased reliability in the monitoring ofcortical activity, level of consciousness, level of sedation, titrationfor seizure treatment, presence of brain dysfunction, and the like. Thesuppression detection methods of the present invention, and the systemsand devices using these methods can be used for anesthesia monitoring,sedation monitoring, brain dysfunction monitoring, seizure treatmenttitration, and the like. These methods and the systems and devices usingthese methods can be used with equipment for the operating room, acutecare such as the intensive care unit, critical care such as theemergency room, or in the field. These methods and the systems anddevices using these methods can be used by anesthesiologists, nurseanesthetists, neurologists and neurosurgeons, pulmonologists, emergencyroom physicians and clinicians, intensive care physicians andclinicians, medics, paramedics, emergency medical technicians,respiratory technicians, and the like. Preferably, these methods and thesystems and devices using these methods can be used by individuals orclinicians with little or no training in signal analysis or processing.These methods preferably are used with anesthesia monitors, sedationmonitors, seizure detectors, sleep diagnostic monitors, any sort of EEGmonitor, battlefield monitors, operating room monitor, ICU monitor,emergency room monitor, and the like.

Various embodiments of the system of the present invention weredeveloped for monitoring and processing various physiological signalsfrom a subject. Preferably, this system is used for the monitoring ofbrain wave or activity from a single patient or multiple patients.Preferably, the system is a multi-channel EEG system; however, dependingon purpose of use and cost, systems may have as few as 1 channel.Preferably, the system also includes one or more methods or algorithmsfor monitoring cortical activity, level of consciousness, level ofsedation, amount of suppression, titration for seizure treatment,presence of brain dysfunction, and the like. Preferably, the system ormonitor can also measure muscle activity, EMG. In addition, the systemand related methods can use other sensors that measure physiologicalsignals which directly or indirectly result in or from braindysfunction, or effect or result from brain activity.

Preferably, the system or monitor is constructed to be rugged, so as towithstand transport, handling and use in all of the applications listedabove including in emergency scenarios, such as on the battlefield or inmass casualty situations, or to reliably survive daily use by emergencymedical personnel or other first responders. The system or monitorshould preferably be splash-proof (or water tight), dust-tight,scratch-resistant, and resistant to mechanical shock and vibration. Thesystem or monitor should preferably be portable and field-deployable inparticular embodiments to a military theater of operation, the scene ofan accident, the home of a patient, or to any clinical setting.

The system described in this invention also preferably incorporates anumber of unique features that improve safety, performance, durability,and reliability. The system should be cardiac defibrillator proof,meaning that its electrical components are capable of withstanding thesurge of electrical current associated with the application of a cardiacdefibrillator shock to a patient being monitored by the system, and thatthe system remains operable after sustaining such a surge. The systemshould have shielded leads so as to reduce as much as possible theeffects of external electromagnetic interference on the collection ofbiopotential or physiological signals from the patient being monitoredby the system. The system should be auto-calibrating, and morepreferably capable of compensating for the potential differences in thegains of the input channels to the patient module. The system should becapable of performing a continuous impedance check on its electrodeleads to ensure the suitability of monitored signals.

Optionally, the system or monitor may be calibrated or tested via theutilization of a “virtual patient” device, which outputs pre-recordeddigital EEG in analog format and in real time in a manner similar towhat would be acquired from an actual patient, possibly with data frompatients with known brain dysfunction or brain wave abnormalities,particularly EEG suppression as a result of neuropathological event orcondition. This virtual patient can also output any arbitrary waveformsat amplitudes similar to those of EEG signals. These waveforms may beused for further testing of the amplification system, such as for thedetermination of the amplifier bandwidth, noise profile, linearity,common mode rejection ratio, or other input requirements.

The system or monitor should preferably be designed for non-expert use.By this, it is meant that a person should not be required to possessextraordinary or special medical training in order to use the systemeffectively and reliably. The system should therefore preferably beautomatic in operation in a number of respects. First, the system shouldbe capable of automatic calibration. Second, the system shouldpreferably have automatic detection of input signal quality; forexample, the system should be capable of detecting an imbalance inelectrode impedance. Third, the system should preferably be capable ofartifact detection and removal on one or more levels, so as to isolatefor analysis that part of the signal which conveys meaningfulinformation related to a subject's cortical activity, level ofconsciousness, level of sedation, amount of suppression, titration forseizure treatment, presence of brain dysfunction, and the like. Fourth,the system should preferably include outputs which result in visualand/or audible feedback capable of informing the user about the state ofthe patient related to monitoring of cortical activity, level ofconsciousness, level of sedation, amount of suppression, titration forseizure treatment, presence of brain dysfunction, and the like at anytime during the period of time that the system is monitoring thepatient.

Preferably, the system should operate in real time. One example ofreal-time operation is the ability of the system to monitor the corticalactivity, level of consciousness, level of sedation, amount ofsuppression, titration for seizure treatment or presence of braindysfunction as it is happening, rather than being limited to analysisthat takes place minutes or hours afterward.

The processor or computer, and the methods of the present inventionpreferably contain software or embedded algorithms or steps thatautomatically identify artifacts and even more preferably remove theartifacts from the physiological signal, and automatically monitorcortical activity, level of consciousness, level of sedation, amount ofsuppression, titration for seizure treatment, presence of braindysfunction, and the like based on the essentially artifact-free EEGsignal.

One embodiment of the present invention involves method of monitoring asubject or patient under anesthesia comprising steps of: acquiring anEEG signal from a subject or patient; computing with a processorsubstantially at the same time as the signal is acquired the firstderivative of the EEG signal; analyzing the first derivative of the EEGsignal to compute at least one suppression detection parameter, the atleast one suppression detection parameter being used to detectsuppression periods in the EEG signal; and outputting a parameter basedat least in part on the suppression detection parameter to a device forcommunicating the outputted parameter to a clinician monitoring thepatient under anesthesia.

Another embodiment of the present invention involves method ofmonitoring a subject or patient under anesthesia comprising steps of:acquiring an EEG signal from a subject or patient; computing with aprocessor substantially at the same time as the signal is acquired thefirst derivative of the EEG signal; analyzing the first derivative ofthe EEG signal to compute at least one suppression detection parameter,the at least one suppression detection parameter being used to detectsuppression periods in the EEG signal; and outputting a parameter basedat least in part on the suppression detection parameter to a device forautomatically controlling the patient's level of anesthesia.

Still another embodiment of the present invention involves a method ofquantifying the amount of EEG suppression in a subject or patientcomprising steps of: acquiring an EEG signal from a subject or patient;computing with a processor substantially at the same time as the signalis acquired the first derivative of the EEG signal; analyzing the firstderivative of the EEG signal to compute at least one suppressiondetection parameter, the at least one suppression detection parameterbeing used to detect suppression periods in the EEG signal; andoutputting a parameter quantifying the amount of suppression in the EEGduring the last minute to a device for communicating the outputtedparameter to a clinician monitoring the patient.

Still another embodiment of the present invention involves a method ofquantifying the amount of EEG suppression in a subject or patientcomprising steps of: acquiring an EEG signal from a subject or patient;computing with a processor substantially at the same time as the signalis acquired the first derivative of the EEG signal; analyzing the firstderivative of the EEG signal and calculating the median absolute valueof the first derivative of the EEG signal; comparing the medianamplitude value of the first derivative of the EEG signal over apre-determined time frame of the signal against a threshold value toidentify periods of suppression; comparing the calculated suppressionperiod length against a pre-determined time threshold; and determiningif the EEG signal is presently suppressed.

Still yet another embodiment of the present invention involves a methodof quantifying the amount of EEG suppression in a subject or patientcomprising steps of: acquiring an EEG signal from a subject or patient;computing with a processor substantially at the same time as the signalis acquired the first derivative of the EEG signal; analyzing the firstderivative of the EEG signal and calculating at least two suppressiondetection parameters, at least one suppression detection parameter beingthe median absolute value of the first derivative of the EEG signal;comparing the suppression detection parameters against a predeterminedthreshold value for each suppression detection parameter to identifyperiods of suppression; comparing the calculated suppression periodlength against a pre-determined time threshold; and determining if theEEG signal is presently suppressed.

Additional features and advantages of the invention will be set forth inthe detailed description which follows, and in part will be readilyapparent to those skilled in the art from that description or recognizedby practicing the invention as described herein, including the detaileddescription which follows, the claims, as well as the appended drawings.

It is to be understood that both the foregoing general description andthe following detailed description are merely exemplary of theinvention, and are intended to provide an overview or framework forunderstanding the nature and character of the invention as it isclaimed. The accompanying drawings are included to provide a furtherunderstanding of the invention, and are incorporated in and constitute apart of this specification. The drawings illustrate various embodimentsof the invention and together with the description serve to explain theprinciples and operation of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A. Block diagram of a system overview for real-time applicationsof the invention wherein various signals, parameters, measurements, andmessages are displayed for a clinician to see and determine the propercourse of treatment.

FIG. 1B. Block diagram of a system overview for real-time applicationsof the invention wherein a closed-loop treatment delivery device is usedto administer medication or treatment when necessary based on level ofconsciousness and amount of suppression occurring.

FIG. 2 . Flowchart depicting one embodiment of the present inventionwherein the first derivative of the EEG signal is analyzed to determinewhether EEG suppression is occurring in a subject.

FIG. 3 . Flowchart depicting one embodiment of the present inventionwherein multiple suppression detection parameters are used to analyzethe first derivative of the EEG signal in order to determine whether EEGsuppression is occurring and information is displayed for user tointerpret and provide appropriate treatment and possibly transmitted toclosed-loop treatment delivery device to automatically administerappropriate medication or treatment.

FIG. 4 . Series of graphs showing the original EEG signal, the firstderivative EEG signal, and the results of three suppression detectionmethods.

DETAILED DESCRIPTION

The present invention relates to the processing of signals, andparticularly to the processing of electrophysiological signals. Moreparticularly, the present invention relates to the processing ofelectroencephalographic signals. More particularly still, the presentinvention relates to the detection and identification of suppressionperiods in electroencephalographic signals. Further, the presentinvention relates to an automated method for identification anddetection of suppression periods in electroencephalographic signals.

All embodiments of the present invention involve acquiring an EEG, otherphysiological signal or other sensor signal from a subject or a patient,the subject being any type of animal, including human subjects. Theprecise method for acquiring a signal from the subject or patient variesaccording to the physiological signal being acquired and analyzed. Inone preferred embodiment, that is acquiring EEG signals, electrodes canbe placed at various locations on the subject's scalp in order to detectEEG or brain wave signals. Common locations for the electrodes includefrontal (F), temporal (T), parietal (P), anterior (A), central (C) andoccipital (0). Preferably for the present invention, at least oneelectrode is placed in the frontal position. Additionally, preferably atleast two electrodes are used—one signal electrode and one referenceelectrode; and if further EEG or brain wave signal channels are desired,the number of electrodes required will depend on whether separatereference electrodes or a single reference electrode is used. Forvarious embodiments of the present invention, preferably an electrode isused and the placement of at least one of the electrodes is at or nearthe frontal lobe of the subject's scalp. In order to obtain a good EEGor brain wave signal, it is desirable to have low impedances for theelectrodes. Typical EEG electrode connections may have impedance in therange of 5 to 10 K ohms. It is in generally desirable to reduce suchimpedance levels to below 2 K ohms. Therefore a conductive paste or gelmay be applied to the electrode to create a connection with impedancebelow 2 K ohms. Alternatively or in conjunction with the conductive gel,the subject's skin may be mechanically abraded, the electrode may beamplified or a dry electrode may be used. Dry physiological recordingelectrodes of the type described in U.S. Pat. No. 7,032,301 are hereinincorporated by reference. Dry electrodes provide the advantage thatthere is no gel to dry out or irritate the skin, which guaranties longshelf life and longer periods of monitoring the subject, no abrading orcleaning of the skin, and that the electrode can be applied in hairyareas such as the scalp.

Other similar methods of acquiring physiological signals may be used inthe present invention which are known to those skilled in the art foracquiring signals such as electrocardiography (ECG), electricalimpedance tomography (EIT), electromyography (EMG) andelectro-oculography (EOG).

In some embodiments, an EEG signal is measured from a subject or patientwho may be having a seizure(s). Similar to above, the patient isattached to an EEG/seizure monitoring system via some form of electrodesand electrode leads. Particularly if the patient is known to be indanger of having a seizure, the EEG signal can be analyzed watching forwaveforms that are indicative of the patient having a seizure so propermedical care can be given. When a seizure is detected, the system can beused to administer drugs or medication to the subject or patient tomaintain a specific level of suppression for therapeutic purposes intreating the seizure.

Other sensor signals measuring physical conditions of the subjectinclude blood pressure measurements, galvanic skin response, respiratoryeffort, respiratory flow, body movement, pulse oximetry, and the like.

Another step in various embodiments involves computing with a processor,substantially at the same time as the signal is acquired, the firstderivative of the physiological or sensor signal. Most embodimentsutilize the first derivative, though some embodiments may use a higherderivative. Utilizing the first derivative rather than the raw (orfiltered) EEG signals has been shown to remove baseline wandering andthus renders the analysis more accurate and reliable. In addition, thesurface EEG signals are the time-varying signals that reflect thefluctuations in the number of activated neurons or the alternatingcomponent of the mean soma potential over time. The first derivative ofEEG signal has been shown to be strongly linked to the mean somapotential based on Cellular Automaton (CA) simulations of corticalfunction. The step of computing the first or second derivative of thesignal is performed on a processor, in real time, and substantially atthe same time as the signal is being acquired. Substantially at the sametime means that immediately as the signal is acquired by the circuitryand apparatus described above, the processor computes the appropriatederivative of that signal.

In various embodiments of the present invention, the acquired EEG signalmay or may not be filtered prior to computing the first (or higher)derivative and analyzing the signal. In those embodiments which requiresignal filtering, such filtering is performed by means of a low-passfilter used to remove high frequency (HF) interference from the EEGsignal. Examples of HF interference that may need to be filtered out ofthe EEG signal include other physiological signals such aselectromyographic (EMG) signals, as well as outside HF interference suchas background electrical noise and noise from electro-surgical units(ESUs). Preferably, when filtering is performed, the low-pass filter isset to allow EEG signals with a frequency of 32 Hz and less to pass.More preferably, the filter allows EEG signals with a frequency of 30 Hzand less to pass. More preferably still, the low-pass filter allows EEGsignals with a frequency of 24 Hz and less to pass. Even morepreferably, the low-pass filter allows EEG signals with a frequency of16 Hz and less to pass.

Another step in various embodiments involves analyzing an epoch ofpredetermined size of the first derivative of the EEG signal using atleast one suppression detection parameter, the at least one suppressiondetection parameter being used to detect suppression in the EEG signal.The suppression detection measure can be virtually any type of operatoror algorithm which is capable of detecting the drastic changes in theEEG signal which may be representative of burst and suppression periods.Such suppression detection measures may include, but are not limited tothe median absolute value, the peak-to-peak time measurement, root meansquare (RMS), spectral measures, entropy measures, energy operators, andthe like. Preferably, at least one suppression detection measure used isthe median absolute value of the first derivative of the EEG signal. Themedian absolute value is a robust measure of the rate of change of EEG,is less sensitive to outliers, and corresponds with the visualrecognition rules of suppression detection. Thus, as the EEG, or otherphysiological signal, is acquired, the processor first calculates thefirst derivative of that signal, and essentially simultaneously computesat least the median absolute value of that first derivative signal.

Still another step in various embodiments of the present inventioninvolves utilizing the above calculated suppression detection measure(i.e., median absolute value) for the detection of suppression periodsin the EEG signal. If the above calculated suppression detection measure(i.e., median absolute value) is below a predetermined threshold for apredetermined amount of time, then that particular epoch is determinedto be suppression.

Some embodiments include a step in which the suppression periods areconfirmed by the existence of burst periods in the last minute to ensurethat low-amplitude EEG activity is not detected as suppression, whilestill making sure that slight EEG activity during suppression periodsbetween the bursts doesn't preclude the detection of suppression. If theabove calculated suppression detection measure (i.e., median absolutevalue) is above another predetermined threshold for a predeterminedamount of time, then that particular epoch is determined to be burst.Preferably, the suppression periods are confirmed based on at least 10seconds of burst in the last minute. More preferably, the suppressionperiods are confirmed based on at least 5 seconds of burst in the lastminute. More preferably still, the suppression periods are confirmedbased on at least 3 seconds of burst in the last minute. Even morepreferably, the suppression periods are confirmed based on at least 1second of burst in the last minute. More preferably yet, the suppressionperiods are confirmed based on at least 0.5 seconds of burst in the lastminute. Even more preferably still, the suppression periods areconfirmed based on at least 0.25 seconds of burst in the last minute.

Some embodiments include a step in which artifacts are detected andidentified, and are not counted as periods of burst activity. Theseartifacts may be those that were not detected by the front-endfiltering, or new additional artifacts that corrupt the signal afterinitial filtering. In this optional step, the present inventiondifferentiates between such artifacts and burst activity. This meansthat the system does not count an aberrant artifact as burst activityand thus effect the detection of burst and suppression periods and theirdurations. This is another step to increase the accuracy of theinvention in environments that create artifacts in the EEG signal.

Some embodiments of the present invention further include a step bywhich the thresholds used for detecting a burst or suppression periodare automatically relaxed or tightened based on environmental factors.For example, if the signal contains a particularly strong or highamplitude period of burst activity, the threshold may be relaxed so theresults of the detection methods are not artificially skewed ormisidentified.

Still further embodiments of the present invention may involve using thesecond-derivative of the EEG signal to perform the suppression detectionmethods. In such embodiments, similar methods may applied whereinsuppression detection measures such as the median absolute value, mean,median, RMS, peak to peak, spectral measures, entropy measures, energyoperators, or the like are used to calculate their respective values,and those values compared against appropriate thresholds to determinewhether suppression or detection is occurring.

Still another step in various embodiments of the present inventioninvolves outputting a signal based at least in part on the occurrence ofsuppression in the EEG signal to a device for communicating theoutputted signal to a clinician monitoring the patient. This outputsignal may be any form of signal designed to get the clinician'sattention and alert her to the fact that suppression has recently or ispresently occurred. Preferably, the output signal is the percentage ofsuppression detected in the EEG signal during the last minute, known assuppression ratio (SR). It may also include audio warnings or alarms, orvisual indicators on a monitor such as a text warning, flashing windows,colors, and the like, or any combination thereof.

Other embodiments may include the step of outputting a signal based atleast in part on the occurrence of suppression in the EEG signal to adevice for controlling the patient's level of anesthesia and amount ofsuppression. In this step, rather than alerting a clinician to theoccurrence of suppression in a subject under anesthesia, the subject isinstead attached to a closed-loop, or semi-closed-loop drug deliverydevice which automatically controls the amount of sedative or anestheticbeing administered to the subject. The steps of the present inventionand method can further include providing a drug or treatment to thesubject or patient to counteract anesthesia and revive the subject orpatient's brain function.

Some embodiments include a step in which artifacts are detected andidentified, and are not counted as periods of burst activity. Theseartifacts may be those that were not detected by the front-endfiltering, or new additional artifacts that corrupt the signal afterinitial filtering. In this optional step, the present inventiondifferentiates between such artifacts and burst activity. This meansthat the system does not count an aberrant artifact as burst activityand thus effect the detection of burst and suppression periods and theirdurations. This is another step to increase the accuracy of theinvention in environments that create artifacts in the EEG signal.

Some embodiments of the present invention further include a step bywhich the thresholds used for determining whether activity is burst orsuppression are automatically relaxed or tightened based onenvironmental factors. For example, if the signal contains aparticularly strong or high amplitude period of burst activity, thethreshold may be relaxed so the results of the detection methods are notartificially skewed or misidentified.

Still further embodiments of the present invention may involve using thesecond-derivative of the EEG signal to perform the suppression detectionmethods. In such embodiments, similar methods may applied whereinsuppression detection measures such as the median absolute value, mean,median, RMS, peak to peak, spectral measures, entropy measures, energyoperators, or the like are used to calculate their respective values,and those values compared against appropriate thresholds to determinewhether suppression or detection is occurring.

Now referring to the FIGS. 1-4 , FIGS. 1A and 1B are block diagrams oftwo possible embodiments of a system for monitoring and real-timetherapy applications. The system shown in FIGS. 1-4 can be adapted withmodifications for other types of sensor signals described within thisapplication. The system can be connected to the subject either on thesubject's scalp 5 with mounted surface electrodes 1, intra-cranialcortical grids 4, or implanted deep brain electrode(s) 3. The electrodeleads 6 are preferably connected to the system via a yoke 2 containingcardiac defibrillation resistors (not shown) designed to absorb theenergy of a cardiac defibrillation pulse. These resistors (not shown)and the associated electronics in the front-end of the instrumentationamplifiers (not shown) are designed to protect the instrumentationelectronics and in particular applications to have electromagneticinterference filters (EMF) to eliminate interference caused by otherelectrical devices, while still ensuring that most of the energydelivered by the pulse is used for the intended therapy.

The brainwave signals are then amplified and digitized by ananalog-digital converter (ADC) circuitry 7. In addition, a signalquality (SQ) circuitry 8, 9 can be used to inject measurement currentsinto the leads 6 in order to calibrate the instrumentation amplifiers(not shown) and measure electrode impedance. Similar SQ circuitry 8, 9monitors the front-end amplifiers in order to detect eventual saturationthat occurs when leads 6 are disconnected. This information, along withthe digitized brainwave signals, is relayed to the processor 10, 11, 12,13, 14.

The processor is composed of the sub-systems 10, 11, 12, 13 14. Thesignal quality assessment module 10 is used to check whether each signalacquired by the system is of sufficient quality to be used in thesubsequent analysis. This is done by continuously measuring theelectrode impedance of each brainwave channel, and by quantifying thelevels of 50 and 60 Hz noise in the signal. High levels of 50 or 60 Hzindicate either a poor electro-magnetic environment, or a poorconnection to the patient which will result in a heightened sensitivityof the system for any other environmental noise (e.g., lead movement,vibration, etc.). High levels of 50 or 60 Hz noise are usuallyindicative of poor signal quality.

If the signal quality is good, the signal enters into the suppressiondetection module 11 of the system. First, the first derivative of theEEG signal is computed. This first derivative is now the signal thatwill be used for further burst and suppression analysis, not the raw (orfiltered) EEG signal. The system utilizes at least one suppressiondetection parameter that will be used to determine if suppression isoccurring in the EEG signal. The suppression measures computation module11 a is where the processor calculates the values of the various (atleast one) suppression detection parameters based on the firstderivative of the acquired EEG signal. Then, once the values for thosemeasures have been calculated, those values are compared against apredetermined threshold value in the suppression detection decisionmodule 11 b. Here, if the suppression detection measure value fallsabove or below (depending on the particular measure in question) thepredetermined threshold value for a predetermined amount of time, thenthe system determines that suppression is present in the EEG signal.

Next, in some embodiments, the system proceeds by analyzing the acquiredsignals in order to detect the presence of environmental orphysiological artifacts (not shown), which may be corrupting the signal.This analysis is performed in the artifact detection and removal module12. Preferably, artifact detection methods and systems such as thosedescribed in U.S. Patent Publication No. 2011-0295142 are used for theartifact detection and removal process, and that publication is hereinincorporated by reference. Preferably, several artifact detectionmethods or algorithms are used in combination. These artifact detectionmethods or algorithms analyze the signal for artifacts usingcombinations of both sensitivity and specificity methods or algorithms,each detecting the presence of artifacts in different ways, and thosemeasures are combined to increase the accuracy of artifact detection inthe combination and decision module (not shown, subset of artifactdetection and removal module 12.

De-noised and artifact-free signals are sent to the brainwaveanalysis/processing module 13. This sub-system derives informationcontained in the signal, such as the level of consciousness of thepatient, the presence of electro-cortical silence, the level of ocularactivity (EOG), the level of muscle activity (EMG), etc. Thisinformation can be used as a complement to the signal acquisition toprovide a better diagnostic means to the user. Some of this informationmay also be used in the signal acquisition and suppression detection totune properly the different thresholds used by the underlying algorithm.

A user interface module 14 provides the means for the user to interactwith the system. In a preferred embodiment, this is done through the useof a display module 15, which can be a touch screen display, or anyother variety of display device. The display module 15 is used toprovide information (i.e., display the physiological signal, variousindexes pertaining to the signal, suppression information, as well asprovide the user a visual representation for inputting information whennecessary). In addition, the user interface module 14 archives all theacquired signals and processed variables into a mass storage device 16,for later review.

The mass storage device 16 is used as a long term storage archive forall of the acquired EEG signals as well as the accompanying processingresults. These data will then be available for later use. The signalswill then be available for historical use and review where clinicians orresearchers can check for burst and suppression, artifacts or otherabnormal brain activity and the like. An artifact free EEG signal can bestored in the mass storage device 16 or a corrupted signal can be storedas well with the artifacts identified as part of the signal.Furthermore, they can be used as a database from which signals can beused for baseline determination or calibration of suppression orartifact detection techniques.

Finally, in some embodiments, the system is connected to a mechanismthat automatically delivers a treatment to the patient, referred in theschematic as the treatment delivery device 18. The output of the systemthrough a processor 10-14 can be used with the treatment delivery deviceincluding a processor 10-14 in closed loop 17, partially closed loop oropen loop to automatically deliver physical, electrical or chemicaltreatment to the subject automatically based on the occurrence ofabnormal brain activity, and monitor the effectiveness of such treatmentin real time.

FIG. 2 is a flowchart of the overall signal acquisition and suppressiondetection process of the present invention. First, in the signalacquisition and filtering step 20, the EEG signal is acquired accordingto the method described above and may optionally have a front-endhardware filter applied to remove any HF interference or otherelectrical noise that may be present and corrupting the signal. Next, inthe signal quality assessment step 22 (a combination of references 7-10in FIGS. 1A and 1B), the incoming signal is checked for quality by meansof impedance measurement of the electrodes. Other methods of signalquality measurement may be used alternatively, or in conjunction withthe impedance measurement. Regardless of the methods of qualitymeasurement used, only a signal with sufficient quality is useful forperforming further analysis. Low quality signals are far more likely toproduce inaccurate results or produce incorrect identifications of burstand suppression periods in the signal. If signal quality is sufficient,however, the system can proceed to the next step of suppressiondetection 11.

The suppression detection phase 11 is the core of the present invention.In all embodiments, the present invention first computes the firstderivative 24 of the EEG (or other physiological) signal. Again, thefirst derivative represents the rate at which the amplitude of the raw(or filtered) EEG signal is changing and thus is useful for alerting theuser or clinician when the amount of fluctuations in the EEG signalincrease or decrease. A rapid decrease in brain activity (somapotential) may be indicative of suppression of the signal and thus thesystem may alert the clinician or user, in an open loop system, orappropriately adjust the level of medication, anesthetic, or sedative ina closed-loop system.

Then, various parameters of the first derivative of the acquired signalare measured and analyzed in the suppression detection measurescomputation step 11 a. At least one suppression detection measure isused to analyze the first derivative of the signal for periods ofsuppression. In all embodiments, the preferred suppression detectionmeasure is the median absolute value of an epoch of predetermined sizeof the first derivative of the signal. When the system computes themedian absolute value of the first derivative of the signal, it iscompared against a predetermined threshold value in the suppressiondetection decision algorithm 11 b. The predetermined threshold is set ata value which has been determined through testing and training of thealgorithm, to be above or at about the maximum value for the medianabsolute value of the first derivative of a suppressed EEG signal.Therefore, if the computed median absolute value is below that thresholdvalue, the suppression detection algorithm determines that particularepoch to be a period of suppression.

Although at least one suppression detection measure (i.e., medianabsolute value) is all that is required, more preferably at least twosuppression detection algorithms are used. More preferably still atleast three suppression detection measures are used. Still morepreferably still at least four suppression detection measures are used.Even more preferably at least five suppression detection measures areused. When more than one suppression detection measure is used todetermine the presence of suppression periods in the signal, again, atleast one is the median absolute value of the first derivative of theacquired signal. In such embodiments, the additional suppressiondetection measures may include, but are not limited to, peak-to-peakamplitude, root mean square (RMS), spectral measures, entropy measures,energy operators, and the like.

The artifact detection and removal algorithm 12 is an optionaladditional step involved in a complete physiological signal recordingsystem. Artifact detection and removal may occur before or aftersuppression detection. Preferably, if artifact detection is used in thesystem, at least two artifact detection algorithms or measures are usedfor providing probabilities of the presence of true and false artifactsin the acquired signal: at least one for sensitivity and at least onefor specificity. Sensitivity methods, processes or algorithms are thosethat are designed to be or happen to be more accurate and useful for thedetection and/or calculation of the presence and/or probability of thepresence of real artifacts in an EEG, other physiological signal orother sensor signal. Specificity methods, processes or algorithms arethose that are designed or happen to be more accurate and useful for thedetection and/or calculation of the absence and/or probability of theabsence of artifacts, in an EEG, other physiological signal, or othersensor signal. Another way to describe these two types of methods,processes or algorithms is that those for sensitivity test for thepercentage of accurate detections when presented with true artifactswhereas those for specificity test for the percentage of accuratenon-detections when presented with a signal with no artifacts.

The signal analysis algorithms 13 are those that perform any furtheranalysis on the signal and additional information is calculated orotherwise retrieved from the signal. Such algorithms may include, butare not limited to those for retrieving other physiological signalactivity (i.e., ocular activity, muscle activity, and the like), levelof consciousness, individual brain hemisphere activity, calculatedindexes corresponding to these values, and any combination of these orother metrics or features that may be drawn from the acquiredphysiological signal.

The display module 15 again is used to show any combination of metrics,signals or features of the signal and analysis that may be relevant ordesired. Such features may include the original physiological signal, afiltered signal, the first or higher derivatives of the acquired signal,event markers accompanying the various signals, signal quality indexes,the suppression index, consciousness index, seizure index and seizureprobability index, warnings and alarms, brain status information,diagnosis and treatment support, and the like. Additionally, the displaymay include audio signals such as beeps, alarms, whistles or the likefor the purpose of alerting the user or clinician of potential problemsor issues.

FIG. 3 is a flow chart depicting the process for suppression detectionas graphically depicted in FIGS. 1A and 1B, wherein the system providesat least signal and suppression information to a display device for useror clinician for evaluation and action, and/or a closed-loop treatmentdelivery device for automatic administration and adjustment ofmedication. The first step is to acquire the EEG signal 20 from thepatient or subject as described above. The EEG (or other physiologicalsignal) may optionally be filtered (not shown) upon acquisition. The rawor filtered signal is then transmitted or transferred to the processor(FIGS. 1A and 1B, references 10-14), which calculates the firstderivative of the acquired signal 24. Once the first derivative iscalculated, the suppression detection measures 26 or algorithms areapplied to that derivative. Other embodiments (not shown) may apply thesuppression detection measures or algorithms to a higher derivative ofthe signal. The suppression detection measures or algorithms, as listedbefore, include at least the median absolute value of the firstderivative of the signal, and may also include other measures including,but not limited to, peak-to-peak amplitude, root mean square (RMS),spectral measures, entropy measures, energy operators, and the like.

Each of the suppression detection measures used produces a value forthat measure corresponding to the appropriate calculation. Those valuesare then compared against predetermined threshold values 28, eachmeasure having its own independent value calculation and correspondingthreshold value. For each suppression detection measure, its calculatedvalue is compared to the predetermined threshold value and adetermination of whether suppression is or was occurring in the acquiredsignal is provided based on whether the calculated suppression detectionmeasure value is above or below the predetermined threshold.

The signal is broken up into discreet sampling windows for purposes ofperforming the analysis. The signal sampling window is preferably about3 seconds long, more preferably about 2 seconds long, even morepreferably about 1 seconds, and most preferably about 0.5 seconds long.Depending on the embodiment, the various suppression detection measuresare calculated 26 for each such sampling window and compared against theappropriate threshold 28 for each particular measure used. If thesuppression detection value is below the threshold for that particularmeasure, then that sampling window is determined to exhibit suppressionand is flagged. This process is then repeated for each subsequentconsecutive signal sampling window, resulting in each window beingeither flagged as exhibiting suppression or cleared as not showingsuppression of the EEG signal. The more consecutive sampling windows inwhich there is suppression detected by the above method, the more likelyit is that actual and potentially harmful suppression is occurring inthe signal.

In some embodiments of the present invention, the detection ofsuppression periods of the signal is accompanied by detection of burstperiods. In such embodiments, the detection measures obtained from thefirst derivative of the acquired physiological (EEG) signal are analyzedfor both the burst and suppression detection. Quite often, suppression,which is the condition being targeted by the present invention, isshortly or immediately preceded by a burst of cortical activity or somapotential. Therefore, some embodiments utilize a burst detectionalgorithm to help increase the accuracy of the suppression detectionalgorithm. Based on the independent detection of burst and suppressionin the first derivative of the signal, the likelihood that the systemaccurately and properly identifies periods of suppression greatlyincreases. In such embodiments, the burst must precede the suppressionperiod. Preferably, for the detected burst period to more accuratelyindicate an oncoming suppression period, the burst period should belonger than 0.5 seconds and at least 1 burst should occur within 1minute preceding a detected suppression period. More preferably, theburst period should be longer than 0.5 seconds and at least 2 burstsshould occur within 1 minute preceding a detected suppression period.Even more preferably, the burst period should be longer than 0.5 secondsand at least 3 bursts should occur within 1 minute preceding a detectedsuppression period. More preferably, still the burst period should belonger than 0.5 seconds and at least 4 bursts should occur within 1minute preceding a detected suppression period. Even still morepreferably, the burst period should be longer than 0.5 seconds and atleast 5 bursts should occur within 1 minute preceding a detectedsuppression period. Most preferably, the burst period should be longerthan 0.5 seconds and at least 6 bursts should occur within 1 minutepreceding a detected suppression period.

Once the suppression detection measures have been calculated andcompared against the threshold value, and the resulting determination ofwhether suppression occurred is made for each sampling window, the nextstep is to determine whether the suppression (assuming it was determinedto have occurred) is of a long enough duration to constitute clinicallyrelevant suppression of the EEG signal. In other words, each individualsampling window may exhibit suppression, but those windows are eachsmall enough that it requires several suppressed samples in consecutivesuccession to warrant a clinical concern for the patient. As notedabove, suppression is typically longer than periods of burst activity,and usually lasts for at least about 5 seconds. Therefore, when onesampling window exhibits suppression, a count is started to determinehow long the detected suppression lasts. That length of suppression timeis compared against a predetermined value 30 which is used to decidewhether actual EEG suppression that is clinically relevant has occurred.

This entire process is preferably performed in real-time as the signalis acquired from the patient. Therefore, as the signal is acquired, theabove analysis is performed and the determination of whether suppressionhas occurred is continuously updated. As these steps are continuouslyperformed, the EEG signal and a suppression index are displayed 15 for aclinician or other user to monitor. These values may be output on ascreen or monitor attached to the monitoring equipment or in closeproximity thereto, or may be sent to a remote location or handhelddevice such as a phone, palm pilot, tablet or other computer. Once theappropriate information, particularly the suppression indexcommunicating to the clinician or other user the level, amount orseverity of any occurring suppression is output or otherwise displayed,the clinician or other user is then able to act accordingly and take thenecessary steps to ensure the patient's safety and recovery, andminimize potential harm to the patient.

Additionally, and optionally, the system may include a closed-looptreatment delivery device 17, 18. Therefore, another optional step is toutilize the suppression detection output to automatically administertreatment or deliver a drug or medication 32 to the patient. In such astep, the suppression index would be used by an automated protocol todetermine the appropriate steps necessary to cause the patient torecover from the prolonged suppression and to carry out that treatment,much as the clinician or user would, but automatically.

FIG. 4 depicts a series of acquired biopotential signals, specificallyEEG signals along with the results of three different suppressiondetection methods. These graphs represent the signal at various phasesin the process and how each method of detecting suppression in thesignal performs relative to the others.

Graph ‘a’ 34 depicts a sample EEG segment as recorded with only thebasic filtering methods described above. No analysis has been performedon the EEG signal, nor any transformation, other than removal offront-end noise and artifacts. The graph depicts the level of brainactivity (vertical axis) over time (horizontal axis). Periods ofsuppression 46 are characterized by very low amplitude brain activity,and are typically interrupted by brief periods of burst activity 44which are characterized by high amplitude, but short-duration brainactivity.

Graph ‘b’ 36 depicts the calculated first derivative of the EEG signalsegment shown in graph ‘a’ 34. As those skilled in the art understand,the first derivative represents the rate at which the signal ischanging, in this case, amplitude. Thus, this graph represents the rateof change of the brain activity (vertical axis) as time (horizontalaxis) passes. The periods of suppression 46 in the first derivativesignal 36 appear largely similar in duration to those shown in the rawEEG signal 34 because during a given period of suppression 46 the signalis not changing amplitude significantly. Conversely, during periods ofburst activity 44, the EEG signal 34 changes quite rapidly and thus thefirst derivative 36 will reflect larger amplitudes of change 48 for theduration of that burst activity 44. This first derivative signal 36 isthe signal on which the present invention performs the analysis andcalculation.

Graphs ‘c’ 38, ‘d’ 40, and ‘e’ 42 each represent the results of adifferent suppression detection method. The vertical axis of each graphis labeled by an ‘s’ and a ‘b’ representing the detection of suppressionperiods 52 and periods of burst activity 50 as determined by the givendetection method. Again, the horizontal axis represents time.

Graph ‘c’ 38 represents the traditional method of visual detection. Asdescribed above, this method requires a trained individual to eitherview a printout of the EEG signal 34 or view the EEG signal 34 on amonitor (not shown) in real time. The signal 34 would be then visuallyinspected for periods of time, typically longer than 0.5 s, where thepeak-to-peak amplitude of the EEG signal is much less (typically 5 to 20μV) and flagged as representing suppression 52 of the signal. Whencompared against the actual EEG signal in graph ‘a’ 34, it is clear thatthis traditional visual method of identifying suppression 52 (and burst50) periods may be quite accurate. The trained user can be veryefficacious in identifying the points where burst 50 and suppression 52occur in the actual EEG signal 34. However, as noted above, thisaccuracy cannot be guaranteed because each individual user may have asubjective opinion of those distinct periods of activity, particularlywhen the signal is not as precise and clean as the one depicted in graph‘a’ 34. Additionally, this visual method is very time intensive,requiring the trained user typically to look at a printout of the signalsignificantly later than the signal is being recorded, as well asexpensive.

Graph ‘d’ 40 represents the results of other automated suppressiondetection methods. Existing automated methods perform the analysis anddetection on the original EEG signal as shown in graph ‘a’ 34, much likethe traditional, visual method. The results of such other automatedmethods clearly show a less accurate detection of suppression periods 52as well as burst periods 50. The suppression period 52 appears to be ofa much longer duration than either the raw signal 34 shows or asidentified by a trained user detecting visually in graph ‘c’. This islikely due to the sensitive nature of the algorithms required to performthe identification in automated systems. When a period of burst activity44 in the original EEG signal 34 ends, it does not do so abruptly. Likeall wave activity, it dampens out rather than simply dropping from highamplitude activity down to a comparably flat signal. This can be seen bylooking at the transition from burst activity 44 to suppression period46 in graph ‘a’ 34. The signal gradually dampens from the high amplitudeburst 44 to the very low amplitude suppression 46. As a result, wherethe trained user is able to discern the apparently subtle differencebetween the burst 44 and suppression 46 in that nebulous dampening area,other automated algorithms cannot obtain the same level of accuracy. Asa result, existing automated detection methods provide artificiallyinflated periods of burst activity 50, and typically artificiallyshortened periods of suppression 52 in the signal. Clearly, such methodsare not nearly as accurate as the traditional method, and although theyare much quicker and cheaper to use, the loss of accuracy can proveharmful to the patient if it leads to misapplication of anesthetics,sedatives or other drugs.

Graph ‘e’ 42 represents the results of the present invention'ssuppression detection methods. These methods utilize the firstderivative 36 of the original EEG signal 34, rather than that originalsignal 34 itself. The results of the present automated method 42 areequivalent in accuracy to the traditional visual method 38. Furthermore,the present automated detection method 42 is not susceptible to thesubjective nature of human review that is prevalent and inherent to thetraditional visual method 38. The burst periods 50 and suppressionperiods 52 as detected by the present automated method 42 are virtuallyidentical to those detected by the trained user using the traditionalvisual method 38. Clearly, both the traditional visual method 38 and thepresent automated method 42 are more accurate than other existingautomated methods. Therefore, it is shown in graph ‘e’ 42 that thepresent automated method is at least as accurate as the most accurateexisting detection method; however, this method further provides theadded benefits of providing that accuracy at a reduced cost (no need topay a trained clinician to sit down and read the EEG signal), and alsois much quicker than the traditional visual method 38, and can even beused in real-time applications. The present method combines all of thebenefits of the other two methods (visual and existing automatedmethods) while eliminating the major drawbacks of each as well.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the present inventionwithout departing from the spirit and scope of the invention. Thus, itis intended that the present invention cover the modifications andvariations of this invention provided they come within the scope of theappended claims and their equivalents.

The invention claims:
 1. A system for monitoring a subject or patientunder anesthesia comprising: at least two electroencephalography (EEG)electrodes adapted for acquiring an EEG signal from the subject orpatient, wherein the EEG signal is a substantially continuous timedomain signal; an analog-digital converter adapted for amplifying anddigitizing the EEG signal a processor configured to apply an algorithmto compute a first derivative of the EEG signal directly from the EEGsignal, configured to determine from the first derivative of the EEGsignal at least one suppression detection parameter from a groupconsisting of: mean absolute value, mean, median, median amplitude, rootmean square (RMS), peak to peak, standard deviation, spectral measures,entropy measures, and energy operators, wherein the processor is furtherconfigured to compare the at least one suppression detection parameteragainst a respective predetermined threshold, and wherein the processoris further configured to determine whether a level of suppression hasoccurred when the at least one detection parameter meets or exceeds therespective predetermined threshold for a predetermined time duration; anoutput module adapted to output the level of suppression detected by theprocessor with the algorithm for the patient or subject underanesthesia; and an anesthesia treatment delivery device for the subjector patient, wherein a medication of the anesthesia treatment deliverydevice is adjusted based, in part, on the level of suppressiondetermination.
 2. The system of claim 1, wherein the at least onesuppression detection parameter used by the algorithm of the processoris the median amplitude of the first derivative of the EEG signal. 3.The system of claim 1, wherein the processor is further configured toapply at least two artifact detection filters and/or algorithms prior tocomputing the first derivative of the EEG signal, at least one artifactdetection and removal filter and/or algorithm for specificity and atleast one artifact detection and removal filter and/or algorithm forsensitivity.
 4. The system of claim 1, wherein the processor isconfigured to apply at least two suppression detection parameters,wherein one suppression detection parameter is the median amplitude ofthe first derivative of the EEG signal, and at least one suppressiondetection parameter is one of RMS, standard deviation, mean, spectralmeasures, entropy measures, and energy operators.
 5. The system of claim1, wherein the output module is configured to provide a warning to aclinician indicating that suppression is occurring in the subject orpatient.
 6. The system of claim 1, wherein the anesthesia treatmentdelivery device is configured to provide a drug or treatment to thesubject or patient, wherein the drug or treatment is adapted tocounteract anesthesia and revive the subject's or patient's brainfunction.
 7. The system of claim 1, wherein the processor is furtherconfigured to detect burst activity utilizing a burst detectionalgorithm, the detected burst activity being indicative of an oncomingsuppression activity when burst activity lasts longer than 0.5 secondsand at least one burst occurs within 1 minute preceding a detectedsuppression period.
 8. A system for monitoring a subject or patientunder anesthesia comprising: at least two electroencephalography (EEG)electrodes adapted for acquiring an EEG signal from the subject orpatient, wherein the EEG signal is a substantially continuous timedomain signal; an analog-digital converter adapted for amplifying anddigitizing the EEG signal; a processor configured to apply a firstalgorithm and a second algorithm, the first algorithm is adapted tomeasure burst activity, and the second algorithm is adapted forcomputing a first derivative of the EEG signal directly from the EEGsignal, and configured to determine, from the first derivative of theEEG signal, at least one suppression detection parameter from a groupconsisting of: mean absolute value, mean, median, median amplitude, rootmean square (RMS), peak to peak, standard deviation, spectral measures,entropy measures, and energy operators, wherein the processor is furtherconfigured to compare the at least one suppression detection parameteragainst a respective predetermined threshold, and wherein the processoris further configured to determine whether a level of suppression hasoccurred when the at least one suppression detection parameter meets orexceeds the respective predetermined threshold for a predetermined timeduration; an output module adapted to output the level of suppressiondetected by the processor with the second algorithm for the patient orsubject under anesthesia; and an anesthesia treatment delivery devicefor the subject or patient, wherein a medication of the anesthesiatreatment delivery device is adjusted based, in part, on the level ofsuppression output; wherein the measured burst activity by the firstalgorithm of the processor is used by the second algorithm as beingindicative of an oncoming suppression activity when burst activity lastslonger than 0.5 seconds and at least one burst occurs within 1 minutepreceding a detected suppression period for determining the level ofsuppression.
 9. The system of claim 8, wherein the at least onesuppression detection parameter used by the second algorithm of theprocessor is the median amplitude of the first derivative of the EEGsignal.
 10. The system of claim 8, wherein the processor is furtherconfigured to apply at least two artifact detection filters and/oralgorithms prior to computing the first derivative of the EEG signal, atleast one artifact detection and removal filter and/or algorithm forspecificity, and at least one artifact detection and removal filterand/or algorithm for sensitivity.
 11. The system of claim 8, wherein theprocessor is configured to apply at least two suppression detectionparameters, wherein one suppression detection parameter is the medianamplitude of the first derivative of the EEG signal, and at least onesuppression detection parameter is one of RMS, standard deviation, mean,spectral measures, entropy measures, and energy operators.
 12. Thesystem of claim 8, wherein the output module is configured to provide awarning to a clinician indicating that suppression is occurring in thesubject or patient.
 13. The system of claim 8, wherein the anesthesiatreatment delivery device is configured to provide a drug or treatmentto the subject or patient or the system further includes a second deviceconfigured to provide the drug or treatment, wherein the drug ortreatment is adapted to counteract the anesthesia treatment and revivethe subject's or patient's brain function.
 14. A system for monitoring asubject or patient under anesthesia comprising: at least twoelectroencephalography (EEG) electrodes adapted for acquiring an EEGsignal from a subject or patient, wherein the EEG signal is asubstantially continuous time domain signal; an analog-digital converteradapted for amplifying and digitizing the EEG signal; a processor withan algorithm configured to compute a first derivative of the EEG signaldirectly from the EEG signal, configured to determine from the firstderivative of the EEG signal at least one suppression detectionparameter from a group consisting of: mean absolute value, mean, median,median amplitude, root mean square (RMS), peak to peak, standarddeviation, spectral measures, entropy measures, and energy operators,wherein the processor is further configured to compare the at least onesuppression detection parameter against a respective a predeterminedthreshold, and wherein the processor is further configured to determinewhether a level of suppression has occurred when the at least onesuppression detection parameter meets or exceeds the respectivepredetermined threshold for a predetermined time duration; an outputmodule adapted to output the level of suppression detected by theprocessor with the algorithm for the patient or subject underanesthesia; and an anesthesia treatment delivery device for the subjector patient, wherein a medication of the anesthesia treatment deliverydevice is adjusted based, in part, on the level of suppressiondetermination; wherein the anesthesia treatment delivery device isconfigured to provide a drug or treatment to the subject or patient orthe system further includes a second device configured to provide thedrug or treatment, wherein the drug or treatment is adapted tocounteract the anesthesia treatment and revive the subject's orpatient's brain function.
 15. The system of claim 14, wherein the atleast one suppression detection parameter used by the algorithm of theprocessor is the median amplitude of the first derivative of the EEGsignal.
 16. The system of claim 14, wherein the processor is furtherconfigured to apply at least two artifact detection filters and/oralgorithms prior to computing the first derivative of the EEG signal, atleast one artifact detection and removal filter and/or algorithm forspecificity and at least one artifact detection and removal filterand/or algorithm for sensitivity.
 17. The system of claim 14, whereinthe processor is configured to apply at least two suppression detectionparameters, wherein one suppression detection parameter is the medianamplitude of the first derivative of the EEG signal, and at least onesuppression detection parameter is one of RMS, standard deviation, mean,spectral measures, entropy measures, and energy operators.
 18. Thesystem of claim 14, wherein the output module is configured to provide awarning to a clinician indicating that suppression is occurring in thesubject or patient.
 19. The system of claim 14, wherein the processor isfurther configured to detect burst activity utilizing a burst detectionalgorithm, the detected burst activity being indicative of an oncomingsuppression activity when burst activity lasts longer than 0.5 secondsand at least one burst occurs within 1 minute preceding a detectedsuppression period.
 20. The system of claim 14, wherein the processor isfurther configured to measure level of consciousness of the patient orsubject.